- name
- pharmaclaw-pharmacology-agent
- description
- Pharmacology agent for ADME/PK profiling of drug candidates from SMILES. Computes drug-likeness (Lipinski Ro5, Veber rules), QED, SA Score, ADME predictions (BBB permeability, aqueous solubility, GI absorption, CYP3A4 inhibition, P-gp substrate, plasma protein binding), and PAINS alerts. Chains from chemistry-query for SMILES input. Triggers on pharmacology, ADME, PK/PD, drug likeness, Lipinski, absorption, distribution, metabolism, excretion, BBB, solubility, bioavailability, lead optimization, drug profiling.
Pharma Pharmacology Agent v2.0.0
Overview
Predictive pharmacology profiling for drug candidates. Combines ADMETlab 3.0 ML predictions (when available) with comprehensive RDKit descriptor-based models. Provides full ADME assessment, toxicity risk, druglikeness scoring, and risk flagging — all from a SMILES string.
Key capabilities:
- Drug-likeness: Lipinski Rule of Five, Veber oral bioavailability rules
- Scores: QED (Quantitative Estimate of Drug-likeness), SA Score (Synthetic Accessibility)
- ADME predictions: BBB permeability, aqueous solubility (ESOL), GI absorption (Egan), CYP3A4 inhibition risk, P-glycoprotein substrate, plasma protein binding
- Safety: PAINS (Pan-Assay Interference) filter alerts
- Risk assessment: Automated flagging of pharmacological concerns
- Standard chain output: JSON schema compatible with all downstream agents
Quick Start
# Profile a molecule from SMILES
exec python scripts/chain_entry.py --input-json '{"smiles": "CC(=O)Oc1ccccc1C(=O)O", "context": "user"}'
# Chain from chemistry-query output
exec python scripts/chain_entry.py --input-json '{"smiles": "<canonical_smiles>", "context": "from_chemistry"}'Scripts
scripts/chain_entry.py
Main entry point. Accepts JSON with smiles field, returns full pharmacology profile.
Input:
{"smiles": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "context": "user"}Output schema:
{
"agent": "pharma-pharmacology",
"version": "1.1.0",
"smiles": "<canonical>",
"status": "success|error",
"report": {
"descriptors": {"mw": 194.08, "logp": -1.03, "tpsa": 61.82, "hbd": 0, "hba": 6, "rotb": 0, "arom_rings": 2, "heavy_atoms": 14, "mr": 51.2},
"lipinski": {"pass": true, "violations": 0, "details": {...}},
"veber": {"pass": true, "tpsa": {...}, "rotatable_bonds": {...}},
"qed": 0.5385,
"sa_score": 2.3,
"adme": {
"bbb": {"prediction": "moderate", "confidence": "medium", "rationale": "..."},
"solubility": {"logS_estimate": -1.87, "class": "high", "rationale": "..."},
"gi_absorption": {"prediction": "high", "rationale": "..."},
"cyp3a4_inhibition": {"risk": "low", "rationale": "..."},
"pgp_substrate": {"prediction": "unlikely", "rationale": "..."},
"plasma_protein_binding": {"prediction": "moderate-low", "rationale": "..."}
},
"pains": {"alert": false}
},
"risks": [],
"recommend_next": ["toxicology", "ip-expansion"],
"confidence": 0.85,
"warnings": [],
"timestamp": "ISO8601"
}ADME Prediction Rules
| Property | Method | Thresholds |
|---|---|---|
| BBB permeability | Clark's rules (TPSA/logP) | TPSA<60+logP 1-3 = high; TPSA<90 = moderate |
| Solubility | ESOL approximation | logS > -2 high; > -4 moderate; else low |
| GI absorption | Egan egg model | logP<5.6 and TPSA<131.6 = high |
| CYP3A4 inhibition | Rule-based | logP>3 and MW>300 = high risk |
| P-gp substrate | Rule-based | MW>400 and HBD>2 = likely |
| Plasma protein binding | logP correlation | logP>3 = high (>90%) |
Chaining
This agent is designed to receive output from chemistry-query:
chemistry-query (name→SMILES+props) → pharma-pharmacology (ADME profile) → toxicology / ip-expansionThe recommend_next field always includes ["toxicology", "ip-expansion"] for pipeline continuation.
Tested With
All features verified end-to-end with RDKit 2024.03+:
| Molecule | MW | logP | Lipinski | Key Findings |
|---|---|---|---|---|
| Caffeine | 194.08 | -1.03 | ✅ Pass (0 violations) | High solubility, moderate BBB, QED 0.54 |
| Aspirin | 180.04 | 1.31 | ✅ Pass (0 violations) | Moderate solubility, SA 1.58 (easy), QED 0.55 |
| Sotorasib | 560.23 | 4.48 | ✅ Pass (1 violation: MW) | Low solubility, CYP3A4 risk, high PPB |
| Metformin | 129.10 | -1.03 | ✅ Pass (0 violations) | High solubility, low BBB, QED 0.25 |
| Invalid SMILES | — | — | — | Graceful JSON error |
| Empty input | — | — | — | Graceful JSON error |
Error Handling
- Invalid SMILES: Returns
status: "error"with descriptive warning - Missing input: Clear error message requesting
smilesorname - All errors produce valid JSON (never crashes)
scripts/admetlab3.py
Enhanced ADME/Tox predictor. Attempts ADMETlab 3.0 API first, falls back to comprehensive RDKit models.
# Full ADME profile
python scripts/admetlab3.py --smiles "CC(=O)Oc1ccccc1C(=O)O"
# Specific categories
python scripts/admetlab3.py --smiles "CN1C=NC2=C1C(=O)N(C(=O)N2C)C" --categories absorption,toxicityOutput includes:
- Physicochemical: MW, LogP, TPSA, LogS (ESOL), solubility class, fraction CSP3, molar refractivity
- Absorption: Lipinski, Veber, Egan, HIA, Caco-2 permeability, P-gp substrate, oral bioavailability
- Distribution: BBB penetration (Clark model), plasma protein binding
- Metabolism: CYP3A4 inhibition risk
- Toxicity: hERG risk, Ames mutagenicity, DILI, structural alerts (nitro, aromatic amine)
- Druglikeness: QED, SA Score, lead-like, drug-like classifications
Resources
references/api_reference.md— API and methodology references
Changelog
v2.0.0 (2026-02-18)
- ADMETlab 3.0 integration (ML-based predictions, auto-fallback to RDKit)
- Enhanced RDKit ADME: Caco-2 permeability, Egan model, HIA, hERG, Ames, DILI
- Solubility via ESOL model
- Lead-like / drug-like classification
- Structural alerts: nitro groups, aromatic amines
v1.1.0 (2026-02-14)
- Initial production release with full ADME profiling
- Lipinski, Veber, QED, SA Score, PAINS
- BBB, solubility, GI absorption, CYP3A4, P-gp, PPB predictions
- Automated risk assessment
- Standard chain output schema
- Comprehensive error handling
- End-to-end tested with diverse molecules